Overview

Dataset statistics

Number of variables27
Number of observations2183
Missing cells18864
Missing cells (%)32.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory527.1 KiB
Average record size in memory247.3 B

Variable types

Numeric7
Categorical14
Boolean4
Unsupported2

Alerts

isic_id has a high cardinality: 2183 distinct valuesHigh cardinality
lesion_id has a high cardinality: 2083 distinct valuesHigh cardinality
patient_id has a high cardinality: 699 distinct valuesHigh cardinality
benign_malignant is highly imbalanced (62.0%)Imbalance
image_type is highly imbalanced (72.0%)Imbalance
mel_ulcer is highly imbalanced (83.1%)Imbalance
acquisition_day has 1362 (62.4%) missing valuesMissing
anatom_site_general has 77 (3.5%) missing valuesMissing
clin_size_long_diam_mm has 1523 (69.8%) missing valuesMissing
dermoscopic_type has 189 (8.7%) missing valuesMissing
diagnosis has 809 (37.1%) missing valuesMissing
diagnosis_confirm_type has 393 (18.0%) missing valuesMissing
mel_class has 2088 (95.6%) missing valuesMissing
mel_thick_mm has 2137 (97.9%) missing valuesMissing
mel_type has 2183 (100.0%) missing valuesMissing
mel_ulcer has 2143 (98.2%) missing valuesMissing
melanocytic has 1582 (72.5%) missing valuesMissing
nevus_type has 2175 (99.6%) missing valuesMissing
mel_mitotic_index has 2183 (100.0%) missing valuesMissing
isic_id is uniformly distributedUniform
lesion_id is uniformly distributedUniform
isic_id has unique valuesUnique
mel_type is an unsupported type, check if it needs cleaning or further analysisUnsupported
mel_mitotic_index is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-08-15 12:01:22.249893
Analysis finished2023-08-15 12:01:28.432131
Duration6.18 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct1282
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean561.87403
Minimum0
Maximum1281
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-08-15T14:01:28.482911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54.1
Q1272.5
median545
Q3818
95-th percentile1171.9
Maximum1281
Range1281
Interquartile range (IQR)545.5

Descriptive statistics

Standard deviation342.34981
Coefficient of variation (CV)0.60929994
Kurtosis-0.93031299
Mean561.87403
Median Absolute Deviation (MAD)273
Skewness0.23231174
Sum1226571
Variance117203.39
MonotonicityNot monotonic
2023-08-15T14:01:28.600108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
0.1%
564 2
 
0.1%
594 2
 
0.1%
595 2
 
0.1%
596 2
 
0.1%
597 2
 
0.1%
598 2
 
0.1%
599 2
 
0.1%
600 2
 
0.1%
601 2
 
0.1%
Other values (1272) 2163
99.1%
ValueCountFrequency (%)
0 2
0.1%
1 2
0.1%
2 2
0.1%
3 2
0.1%
4 2
0.1%
5 2
0.1%
6 2
0.1%
7 2
0.1%
8 2
0.1%
9 2
0.1%
ValueCountFrequency (%)
1281 1
< 0.1%
1280 1
< 0.1%
1279 1
< 0.1%
1278 1
< 0.1%
1277 1
< 0.1%
1276 1
< 0.1%
1275 1
< 0.1%
1274 1
< 0.1%
1273 1
< 0.1%
1272 1
< 0.1%

isic_id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct2183
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size34.1 KiB
ISIC_3079785
 
1
ISIC_7428306
 
1
ISIC_5905688
 
1
ISIC_9186977
 
1
ISIC_7316506
 
1
Other values (2178)
2178 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters26196
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2183 ?
Unique (%)100.0%

Sample

1st rowISIC_3079785
2nd rowISIC_2107859
3rd rowISIC_3443621
4th rowISIC_2368449
5th rowISIC_0094098

Common Values

ValueCountFrequency (%)
ISIC_3079785 1
 
< 0.1%
ISIC_7428306 1
 
< 0.1%
ISIC_5905688 1
 
< 0.1%
ISIC_9186977 1
 
< 0.1%
ISIC_7316506 1
 
< 0.1%
ISIC_3567510 1
 
< 0.1%
ISIC_7246266 1
 
< 0.1%
ISIC_4511704 1
 
< 0.1%
ISIC_7549843 1
 
< 0.1%
ISIC_9500805 1
 
< 0.1%
Other values (2173) 2173
99.5%

Length

2023-08-15T14:01:28.703547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
isic_3079785 1
 
< 0.1%
isic_4326975 1
 
< 0.1%
isic_0942544 1
 
< 0.1%
isic_6168031 1
 
< 0.1%
isic_3550047 1
 
< 0.1%
isic_1714551 1
 
< 0.1%
isic_1497097 1
 
< 0.1%
isic_5911355 1
 
< 0.1%
isic_8793798 1
 
< 0.1%
isic_3983413 1
 
< 0.1%
Other values (2173) 2173
99.5%

Most occurring characters

ValueCountFrequency (%)
I 4366
16.7%
S 2183
 
8.3%
C 2183
 
8.3%
_ 2183
 
8.3%
7 1568
 
6.0%
5 1568
 
6.0%
6 1560
 
6.0%
8 1556
 
5.9%
0 1552
 
5.9%
3 1537
 
5.9%
Other values (4) 5940
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15281
58.3%
Uppercase Letter 8732
33.3%
Connector Punctuation 2183
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 1568
10.3%
5 1568
10.3%
6 1560
10.2%
8 1556
10.2%
0 1552
10.2%
3 1537
10.1%
1 1533
10.0%
4 1503
9.8%
9 1493
9.8%
2 1411
9.2%
Uppercase Letter
ValueCountFrequency (%)
I 4366
50.0%
S 2183
25.0%
C 2183
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17464
66.7%
Latin 8732
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 2183
12.5%
7 1568
9.0%
5 1568
9.0%
6 1560
8.9%
8 1556
8.9%
0 1552
8.9%
3 1537
8.8%
1 1533
8.8%
4 1503
8.6%
9 1493
8.5%
Latin
ValueCountFrequency (%)
I 4366
50.0%
S 2183
25.0%
C 2183
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 4366
16.7%
S 2183
 
8.3%
C 2183
 
8.3%
_ 2183
 
8.3%
7 1568
 
6.0%
5 1568
 
6.0%
6 1560
 
6.0%
8 1556
 
5.9%
0 1552
 
5.9%
3 1537
 
5.9%
Other values (4) 5940
22.7%

attribution
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.1 KiB
The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre
821 
Hospital Italiano de Buenos Aires
761 
Memorial Sloan Kettering Cancer Center
601 

Length

Max length108
Median length38
Mean length62.583142
Min length33

Characters and Unicode

Total characters136619
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHospital Italiano de Buenos Aires
2nd rowHospital Italiano de Buenos Aires
3rd rowHospital Italiano de Buenos Aires
4th rowHospital Italiano de Buenos Aires
5th rowHospital Italiano de Buenos Aires

Common Values

ValueCountFrequency (%)
The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre 821
37.6%
Hospital Italiano de Buenos Aires 761
34.9%
Memorial Sloan Kettering Cancer Center 601
27.5%

Length

2023-08-15T14:01:28.797777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T14:01:28.911570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
the 1642
 
9.4%
of 1642
 
9.4%
queensland 1642
 
9.4%
university 1642
 
9.4%
diamantina 821
 
4.7%
institute 821
 
4.7%
dermatology 821
 
4.7%
research 821
 
4.7%
centre 821
 
4.7%
buenos 761
 
4.4%
Other values (9) 6049
34.6%

Most occurring characters

ValueCountFrequency (%)
e 17383
12.7%
15300
11.2%
n 12136
 
8.9%
t 9893
 
7.2%
a 9833
 
7.2%
i 9232
 
6.8%
r 7270
 
5.3%
s 7209
 
5.3%
o 6769
 
5.0%
l 5187
 
3.8%
Other values (24) 36407
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 104597
76.6%
Space Separator 15300
 
11.2%
Uppercase Letter 15080
 
11.0%
Other Punctuation 1642
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17383
16.6%
n 12136
11.6%
t 9893
9.5%
a 9833
9.4%
i 9232
8.8%
r 7270
7.0%
s 7209
6.9%
o 6769
 
6.5%
l 5187
 
5.0%
u 3224
 
3.1%
Other values (9) 16461
15.7%
Uppercase Letter
ValueCountFrequency (%)
C 2023
13.4%
D 1642
10.9%
T 1642
10.9%
Q 1642
10.9%
U 1642
10.9%
I 1582
10.5%
R 821
5.4%
H 761
 
5.0%
B 761
 
5.0%
A 761
 
5.0%
Other values (3) 1803
12.0%
Space Separator
ValueCountFrequency (%)
15300
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119677
87.6%
Common 16942
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17383
14.5%
n 12136
 
10.1%
t 9893
 
8.3%
a 9833
 
8.2%
i 9232
 
7.7%
r 7270
 
6.1%
s 7209
 
6.0%
o 6769
 
5.7%
l 5187
 
4.3%
u 3224
 
2.7%
Other values (22) 31541
26.4%
Common
ValueCountFrequency (%)
15300
90.3%
, 1642
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136619
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17383
12.7%
15300
11.2%
n 12136
 
8.9%
t 9893
 
7.2%
a 9833
 
7.2%
i 9232
 
6.8%
r 7270
 
5.3%
s 7209
 
5.3%
o 6769
 
5.0%
l 5187
 
3.8%
Other values (24) 36407
26.6%

copyright_license
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.1 KiB
CC-BY
821 
CC-BY-NC
761 
CC-0
601 

Length

Max length8
Median length5
Mean length5.7704993
Min length4

Characters and Unicode

Total characters12597
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCC-BY-NC
2nd rowCC-BY-NC
3rd rowCC-BY-NC
4th rowCC-BY-NC
5th rowCC-BY-NC

Common Values

ValueCountFrequency (%)
CC-BY 821
37.6%
CC-BY-NC 761
34.9%
CC-0 601
27.5%

Length

2023-08-15T14:01:29.018099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T14:01:29.132299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
cc-by 821
37.6%
cc-by-nc 761
34.9%
cc-0 601
27.5%

Most occurring characters

ValueCountFrequency (%)
C 5127
40.7%
- 2944
23.4%
B 1582
 
12.6%
Y 1582
 
12.6%
N 761
 
6.0%
0 601
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9052
71.9%
Dash Punctuation 2944
 
23.4%
Decimal Number 601
 
4.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 5127
56.6%
B 1582
 
17.5%
Y 1582
 
17.5%
N 761
 
8.4%
Dash Punctuation
ValueCountFrequency (%)
- 2944
100.0%
Decimal Number
ValueCountFrequency (%)
0 601
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9052
71.9%
Common 3545
 
28.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 5127
56.6%
B 1582
 
17.5%
Y 1582
 
17.5%
N 761
 
8.4%
Common
ValueCountFrequency (%)
- 2944
83.0%
0 601
 
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 5127
40.7%
- 2944
23.4%
B 1582
 
12.6%
Y 1582
 
12.6%
N 761
 
6.0%
0 601
 
4.8%

acquisition_day
Real number (ℝ)

Distinct25
Distinct (%)3.0%
Missing1362
Missing (%)62.4%
Infinite0
Infinite (%)0.0%
Mean107.42509
Minimum1
Maximum1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-08-15T14:01:29.219485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q3138
95-th percentile638
Maximum1017
Range1016
Interquartile range (IQR)137

Descriptive statistics

Standard deviation215.44389
Coefficient of variation (CV)2.0055267
Kurtosis4.8639441
Mean107.42509
Median Absolute Deviation (MAD)0
Skewness2.2794308
Sum88196
Variance46416.072
MonotonicityNot monotonic
2023-08-15T14:01:29.316333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 600
27.5%
348 36
 
1.6%
478 36
 
1.6%
180 26
 
1.2%
638 18
 
0.8%
799 16
 
0.7%
221 13
 
0.6%
92 11
 
0.5%
1017 11
 
0.5%
138 8
 
0.4%
Other values (15) 46
 
2.1%
(Missing) 1362
62.4%
ValueCountFrequency (%)
1 600
27.5%
92 11
 
0.5%
121 1
 
< 0.1%
128 1
 
< 0.1%
138 8
 
0.4%
148 4
 
0.2%
162 7
 
0.3%
169 1
 
< 0.1%
180 26
 
1.2%
213 6
 
0.3%
ValueCountFrequency (%)
1017 11
 
0.5%
799 16
0.7%
672 1
 
< 0.1%
666 1
 
< 0.1%
638 18
0.8%
478 36
1.6%
465 2
 
0.1%
463 3
 
0.1%
352 4
 
0.2%
348 36
1.6%

age_approx
Real number (ℝ)

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.694915
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-08-15T14:01:29.404660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q135
median50
Q365
95-th percentile75
Maximum85
Range65
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.566932
Coefficient of variation (CV)0.32047509
Kurtosis-1.0741166
Mean51.694915
Median Absolute Deviation (MAD)15
Skewness0.022696038
Sum112850
Variance274.46325
MonotonicityNot monotonic
2023-08-15T14:01:29.488197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
35 280
12.8%
50 250
11.5%
65 232
10.6%
55 205
9.4%
70 191
8.7%
30 181
8.3%
75 171
7.8%
40 171
7.8%
60 142
6.5%
45 130
6.0%
Other values (4) 230
10.5%
ValueCountFrequency (%)
20 49
 
2.2%
25 77
 
3.5%
30 181
8.3%
35 280
12.8%
40 171
7.8%
45 130
6.0%
50 250
11.5%
55 205
9.4%
60 142
6.5%
65 232
10.6%
ValueCountFrequency (%)
85 27
 
1.2%
80 77
 
3.5%
75 171
7.8%
70 191
8.7%
65 232
10.6%
60 142
6.5%
55 205
9.4%
50 250
11.5%
45 130
6.0%
40 171
7.8%

anatom_site_general
Categorical

Distinct8
Distinct (%)0.4%
Missing77
Missing (%)3.5%
Memory size34.1 KiB
posterior torso
633 
lower extremity
419 
anterior torso
375 
upper extremity
372 
head/neck
220 
Other values (3)
87 

Length

Max length15
Median length15
Mean length14.094492
Min length9

Characters and Unicode

Total characters29683
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlower extremity
2nd rowhead/neck
3rd rowhead/neck
4th rowhead/neck
5th rowposterior torso

Common Values

ValueCountFrequency (%)
posterior torso 633
29.0%
lower extremity 419
19.2%
anterior torso 375
17.2%
upper extremity 372
17.0%
head/neck 220
 
10.1%
lateral torso 65
 
3.0%
palms/soles 16
 
0.7%
oral/genital 6
 
0.3%
(Missing) 77
 
3.5%

Length

2023-08-15T14:01:29.589926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T14:01:29.711668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
torso 1073
27.0%
extremity 791
19.9%
posterior 633
15.9%
lower 419
 
10.6%
anterior 375
 
9.4%
upper 372
 
9.4%
head/neck 220
 
5.5%
lateral 65
 
1.6%
palms/soles 16
 
0.4%
oral/genital 6
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 4742
16.0%
o 4228
14.2%
e 3908
13.2%
t 3734
12.6%
1864
 
6.3%
i 1805
 
6.1%
s 1754
 
5.9%
p 1393
 
4.7%
m 807
 
2.7%
x 791
 
2.7%
Other values (12) 4657
15.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27577
92.9%
Space Separator 1864
 
6.3%
Other Punctuation 242
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 4742
17.2%
o 4228
15.3%
e 3908
14.2%
t 3734
13.5%
i 1805
 
6.5%
s 1754
 
6.4%
p 1393
 
5.1%
m 807
 
2.9%
x 791
 
2.9%
y 791
 
2.9%
Other values (10) 3624
13.1%
Space Separator
ValueCountFrequency (%)
1864
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27577
92.9%
Common 2106
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 4742
17.2%
o 4228
15.3%
e 3908
14.2%
t 3734
13.5%
i 1805
 
6.5%
s 1754
 
6.4%
p 1393
 
5.1%
m 807
 
2.9%
x 791
 
2.9%
y 791
 
2.9%
Other values (10) 3624
13.1%
Common
ValueCountFrequency (%)
1864
88.5%
/ 242
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29683
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 4742
16.0%
o 4228
14.2%
e 3908
13.2%
t 3734
12.6%
1864
 
6.3%
i 1805
 
6.1%
s 1754
 
5.9%
p 1393
 
4.7%
m 807
 
2.7%
x 791
 
2.7%
Other values (12) 4657
15.7%

benign_malignant
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.1 KiB
benign
1820 
malignant
317 
indeterminate/benign
 
32
indeterminate/malignant
 
14

Length

Max length23
Median length6
Mean length6.7498855
Min length6

Characters and Unicode

Total characters14735
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbenign
2nd rowmalignant
3rd rowbenign
4th rowmalignant
5th rowmalignant

Common Values

ValueCountFrequency (%)
benign 1820
83.4%
malignant 317
 
14.5%
indeterminate/benign 32
 
1.5%
indeterminate/malignant 14
 
0.6%

Length

2023-08-15T14:01:29.825293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T14:01:29.930966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
benign 1820
83.4%
malignant 317
 
14.5%
indeterminate/benign 32
 
1.5%
indeterminate/malignant 14
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n 4458
30.3%
i 2275
15.4%
g 2183
14.8%
e 1990
13.5%
b 1852
12.6%
a 708
 
4.8%
t 423
 
2.9%
m 377
 
2.6%
l 331
 
2.2%
d 46
 
0.3%
Other values (2) 92
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14689
99.7%
Other Punctuation 46
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 4458
30.3%
i 2275
15.5%
g 2183
14.9%
e 1990
13.5%
b 1852
12.6%
a 708
 
4.8%
t 423
 
2.9%
m 377
 
2.6%
l 331
 
2.3%
d 46
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14689
99.7%
Common 46
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 4458
30.3%
i 2275
15.5%
g 2183
14.9%
e 1990
13.5%
b 1852
12.6%
a 708
 
4.8%
t 423
 
2.9%
m 377
 
2.6%
l 331
 
2.3%
d 46
 
0.3%
Common
ValueCountFrequency (%)
/ 46
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 4458
30.3%
i 2275
15.4%
g 2183
14.8%
e 1990
13.5%
b 1852
12.6%
a 708
 
4.8%
t 423
 
2.9%
m 377
 
2.6%
l 331
 
2.2%
d 46
 
0.3%
Other values (2) 92
 
0.6%

clin_size_long_diam_mm
Real number (ℝ)

Distinct133
Distinct (%)20.2%
Missing1523
Missing (%)69.8%
Infinite0
Infinite (%)0.0%
Mean6.4613636
Minimum1.1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-08-15T14:01:30.033202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.7
Q14.2
median5.7
Q37.9
95-th percentile12.8
Maximum20
Range18.9
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation3.2600038
Coefficient of variation (CV)0.50453805
Kurtosis2.8071429
Mean6.4613636
Median Absolute Deviation (MAD)1.7
Skewness1.4603568
Sum4264.5
Variance10.627625
MonotonicityNot monotonic
2023-08-15T14:01:30.144882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1 17
 
0.8%
4.8 16
 
0.7%
5 15
 
0.7%
4.4 14
 
0.6%
5.4 13
 
0.6%
3.6 13
 
0.6%
4.9 12
 
0.5%
5.3 12
 
0.5%
6.8 12
 
0.5%
5.6 12
 
0.5%
Other values (123) 524
 
24.0%
(Missing) 1523
69.8%
ValueCountFrequency (%)
1.1 1
 
< 0.1%
1.4 2
0.1%
1.5 2
0.1%
1.6 1
 
< 0.1%
1.7 1
 
< 0.1%
1.8 3
0.1%
1.9 3
0.1%
2.1 3
0.1%
2.2 4
0.2%
2.3 3
0.1%
ValueCountFrequency (%)
20 2
0.1%
19.7 1
< 0.1%
19.4 1
< 0.1%
19.1 2
0.1%
19 1
< 0.1%
17.9 2
0.1%
17.5 1
< 0.1%
17.2 2
0.1%
17 1
< 0.1%
16 1
< 0.1%

dermoscopic_type
Categorical

Distinct3
Distinct (%)0.2%
Missing189
Missing (%)8.7%
Memory size34.1 KiB
contact polarized
1112 
contact non-polarized
836 
non-contact polarized
 
46

Length

Max length21
Median length17
Mean length18.769308
Min length17

Characters and Unicode

Total characters37426
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcontact polarized
2nd rowcontact polarized
3rd rowcontact polarized
4th rowcontact polarized
5th rowcontact polarized

Common Values

ValueCountFrequency (%)
contact polarized 1112
50.9%
contact non-polarized 836
38.3%
non-contact polarized 46
 
2.1%
(Missing) 189
 
8.7%

Length

2023-08-15T14:01:30.261879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T14:01:30.377026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
contact 1948
48.8%
polarized 1158
29.0%
non-polarized 836
21.0%
non-contact 46
 
1.2%

Most occurring characters

ValueCountFrequency (%)
o 4870
13.0%
c 3988
10.7%
t 3988
10.7%
a 3988
10.7%
n 3758
10.0%
1994
 
5.3%
p 1994
 
5.3%
l 1994
 
5.3%
r 1994
 
5.3%
i 1994
 
5.3%
Other values (4) 6864
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34550
92.3%
Space Separator 1994
 
5.3%
Dash Punctuation 882
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4870
14.1%
c 3988
11.5%
t 3988
11.5%
a 3988
11.5%
n 3758
10.9%
p 1994
5.8%
l 1994
5.8%
r 1994
5.8%
i 1994
5.8%
z 1994
5.8%
Other values (2) 3988
11.5%
Space Separator
ValueCountFrequency (%)
1994
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34550
92.3%
Common 2876
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4870
14.1%
c 3988
11.5%
t 3988
11.5%
a 3988
11.5%
n 3758
10.9%
p 1994
5.8%
l 1994
5.8%
r 1994
5.8%
i 1994
5.8%
z 1994
5.8%
Other values (2) 3988
11.5%
Common
ValueCountFrequency (%)
1994
69.3%
- 882
30.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 4870
13.0%
c 3988
10.7%
t 3988
10.7%
a 3988
10.7%
n 3758
10.0%
1994
 
5.3%
p 1994
 
5.3%
l 1994
 
5.3%
r 1994
 
5.3%
i 1994
 
5.3%
Other values (4) 6864
18.3%

diagnosis
Categorical

Distinct17
Distinct (%)1.2%
Missing809
Missing (%)37.1%
Memory size34.1 KiB
nevus
788 
melanoma
205 
basal cell carcinoma
81 
seborrheic keratosis
 
65
lentigo NOS
 
50
Other values (12)
185 

Length

Max length34
Median length5
Mean length9.3231441
Min length4

Characters and Unicode

Total characters12810
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rownevus
2nd rowmelanoma
3rd rowsolar lentigo
4th rowmelanoma
5th rowmelanoma

Common Values

ValueCountFrequency (%)
nevus 788
36.1%
melanoma 205
 
9.4%
basal cell carcinoma 81
 
3.7%
seborrheic keratosis 65
 
3.0%
lentigo NOS 50
 
2.3%
squamous cell carcinoma 45
 
2.1%
actinic keratosis 30
 
1.4%
atypical melanocytic proliferation 28
 
1.3%
dermatofibroma 26
 
1.2%
lichenoid keratosis 23
 
1.1%
Other values (7) 33
 
1.5%
(Missing) 809
37.1%

Length

2023-08-15T14:01:30.472612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nevus 788
41.9%
melanoma 205
 
10.9%
cell 127
 
6.7%
carcinoma 126
 
6.7%
keratosis 118
 
6.3%
basal 81
 
4.3%
seborrheic 65
 
3.5%
lentigo 59
 
3.1%
nos 50
 
2.7%
squamous 45
 
2.4%
Other values (18) 218
 
11.6%

Most occurring characters

ValueCountFrequency (%)
e 1554
12.1%
n 1308
10.2%
s 1307
10.2%
a 1238
9.7%
u 900
 
7.0%
o 810
 
6.3%
v 808
 
6.3%
l 753
 
5.9%
m 664
 
5.2%
c 634
 
4.9%
Other values (16) 2834
22.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12152
94.9%
Space Separator 508
 
4.0%
Uppercase Letter 150
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1554
12.8%
n 1308
10.8%
s 1307
10.8%
a 1238
10.2%
u 900
7.4%
o 810
 
6.7%
v 808
 
6.6%
l 753
 
6.2%
m 664
 
5.5%
c 634
 
5.2%
Other values (12) 2176
17.9%
Uppercase Letter
ValueCountFrequency (%)
N 50
33.3%
O 50
33.3%
S 50
33.3%
Space Separator
ValueCountFrequency (%)
508
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12302
96.0%
Common 508
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1554
12.6%
n 1308
10.6%
s 1307
10.6%
a 1238
10.1%
u 900
 
7.3%
o 810
 
6.6%
v 808
 
6.6%
l 753
 
6.1%
m 664
 
5.4%
c 634
 
5.2%
Other values (15) 2326
18.9%
Common
ValueCountFrequency (%)
508
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1554
12.1%
n 1308
10.2%
s 1307
10.2%
a 1238
9.7%
u 900
 
7.0%
o 810
 
6.3%
v 808
 
6.3%
l 753
 
5.9%
m 664
 
5.2%
c 634
 
4.9%
Other values (16) 2834
22.1%

diagnosis_confirm_type
Categorical

Distinct3
Distinct (%)0.2%
Missing393
Missing (%)18.0%
Memory size34.1 KiB
histopathology
980 
serial imaging showing no change
806 
single image expert consensus
 
4

Length

Max length32
Median length14
Mean length22.138547
Min length14

Characters and Unicode

Total characters39628
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhistopathology
2nd rowhistopathology
3rd rowhistopathology
4th rowhistopathology
5th rowhistopathology

Common Values

ValueCountFrequency (%)
histopathology 980
44.9%
serial imaging showing no change 806
36.9%
single image expert consensus 4
 
0.2%
(Missing) 393
18.0%

Length

2023-08-15T14:01:30.573111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T14:01:30.680306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
histopathology 980
19.5%
serial 806
16.0%
imaging 806
16.0%
showing 806
16.0%
no 806
16.0%
change 806
16.0%
single 4
 
0.1%
image 4
 
0.1%
expert 4
 
0.1%
consensus 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 4556
11.5%
g 4212
10.6%
i 4212
10.6%
h 3572
9.0%
a 3402
8.6%
3236
8.2%
n 3236
8.2%
s 2608
 
6.6%
t 1964
 
5.0%
l 1790
 
4.5%
Other values (9) 6840
17.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36392
91.8%
Space Separator 3236
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4556
12.5%
g 4212
11.6%
i 4212
11.6%
h 3572
9.8%
a 3402
9.3%
n 3236
8.9%
s 2608
7.2%
t 1964
 
5.4%
l 1790
 
4.9%
e 1632
 
4.5%
Other values (8) 5208
14.3%
Space Separator
ValueCountFrequency (%)
3236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36392
91.8%
Common 3236
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4556
12.5%
g 4212
11.6%
i 4212
11.6%
h 3572
9.8%
a 3402
9.3%
n 3236
8.9%
s 2608
7.2%
t 1964
 
5.4%
l 1790
 
4.9%
e 1632
 
4.5%
Other values (8) 5208
14.3%
Common
ValueCountFrequency (%)
3236
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 4556
11.5%
g 4212
10.6%
i 4212
10.6%
h 3572
9.0%
a 3402
8.6%
3236
8.2%
n 3236
8.2%
s 2608
 
6.6%
t 1964
 
5.0%
l 1790
 
4.5%
Other values (9) 6840
17.3%

family_hx_mm
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.2 KiB
False
1282 
True
901 
ValueCountFrequency (%)
False 1282
58.7%
True 901
41.3%
2023-08-15T14:01:30.778280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

image_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.1 KiB
dermoscopic
2077 
clinical
 
106

Length

Max length11
Median length11
Mean length10.854329
Min length8

Characters and Unicode

Total characters23695
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdermoscopic
2nd rowclinical
3rd rowdermoscopic
4th rowdermoscopic
5th rowclinical

Common Values

ValueCountFrequency (%)
dermoscopic 2077
95.1%
clinical 106
 
4.9%

Length

2023-08-15T14:01:30.864214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T14:01:30.966021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
dermoscopic 2077
95.1%
clinical 106
 
4.9%

Most occurring characters

ValueCountFrequency (%)
c 4366
18.4%
o 4154
17.5%
i 2289
9.7%
d 2077
8.8%
e 2077
8.8%
r 2077
8.8%
m 2077
8.8%
s 2077
8.8%
p 2077
8.8%
l 212
 
0.9%
Other values (2) 212
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23695
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 4366
18.4%
o 4154
17.5%
i 2289
9.7%
d 2077
8.8%
e 2077
8.8%
r 2077
8.8%
m 2077
8.8%
s 2077
8.8%
p 2077
8.8%
l 212
 
0.9%
Other values (2) 212
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 23695
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 4366
18.4%
o 4154
17.5%
i 2289
9.7%
d 2077
8.8%
e 2077
8.8%
r 2077
8.8%
m 2077
8.8%
s 2077
8.8%
p 2077
8.8%
l 212
 
0.9%
Other values (2) 212
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 4366
18.4%
o 4154
17.5%
i 2289
9.7%
d 2077
8.8%
e 2077
8.8%
r 2077
8.8%
m 2077
8.8%
s 2077
8.8%
p 2077
8.8%
l 212
 
0.9%
Other values (2) 212
 
0.9%

lesion_id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct2083
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size34.1 KiB
IL_6821221
 
6
IL_2055980
 
4
IL_7051641
 
4
IL_8955295
 
3
IL_6361168
 
2
Other values (2078)
2164 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters21830
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1992 ?
Unique (%)91.3%

Sample

1st rowIL_3797557
2nd rowIL_3211111
3rd rowIL_3949403
4th rowIL_3211111
5th rowIL_6961144

Common Values

ValueCountFrequency (%)
IL_6821221 6
 
0.3%
IL_2055980 4
 
0.2%
IL_7051641 4
 
0.2%
IL_8955295 3
 
0.1%
IL_6361168 2
 
0.1%
IL_5870994 2
 
0.1%
IL_2147560 2
 
0.1%
IL_3440737 2
 
0.1%
IL_5806562 2
 
0.1%
IL_0729649 2
 
0.1%
Other values (2073) 2154
98.7%

Length

2023-08-15T14:01:31.044509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
il_6821221 6
 
0.3%
il_7051641 4
 
0.2%
il_2055980 4
 
0.2%
il_8955295 3
 
0.1%
il_3250161 2
 
0.1%
il_4757470 2
 
0.1%
il_7949381 2
 
0.1%
il_5502639 2
 
0.1%
il_9366956 2
 
0.1%
il_2965116 2
 
0.1%
Other values (2073) 2154
98.7%

Most occurring characters

ValueCountFrequency (%)
I 2183
10.0%
L 2183
10.0%
_ 2183
10.0%
1 1589
 
7.3%
3 1578
 
7.2%
6 1574
 
7.2%
0 1559
 
7.1%
9 1528
 
7.0%
7 1528
 
7.0%
5 1507
 
6.9%
Other values (3) 4418
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15281
70.0%
Uppercase Letter 4366
 
20.0%
Connector Punctuation 2183
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1589
10.4%
3 1578
10.3%
6 1574
10.3%
0 1559
10.2%
9 1528
10.0%
7 1528
10.0%
5 1507
9.9%
2 1504
9.8%
4 1483
9.7%
8 1431
9.4%
Uppercase Letter
ValueCountFrequency (%)
I 2183
50.0%
L 2183
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17464
80.0%
Latin 4366
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 2183
12.5%
1 1589
9.1%
3 1578
9.0%
6 1574
9.0%
0 1559
8.9%
9 1528
8.7%
7 1528
8.7%
5 1507
8.6%
2 1504
8.6%
4 1483
8.5%
Latin
ValueCountFrequency (%)
I 2183
50.0%
L 2183
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 2183
10.0%
L 2183
10.0%
_ 2183
10.0%
1 1589
 
7.3%
3 1578
 
7.2%
6 1574
 
7.2%
0 1559
 
7.1%
9 1528
 
7.0%
7 1528
 
7.0%
5 1507
 
6.9%
Other values (3) 4418
20.2%

mel_class
Categorical

Distinct2
Distinct (%)2.1%
Missing2088
Missing (%)95.6%
Memory size34.1 KiB
melanoma in situ
49 
invasive melanoma
46 

Length

Max length17
Median length16
Mean length16.484211
Min length16

Characters and Unicode

Total characters1566
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowinvasive melanoma
2nd rowmelanoma in situ
3rd rowmelanoma in situ
4th rowmelanoma in situ
5th rowmelanoma in situ

Common Values

ValueCountFrequency (%)
melanoma in situ 49
 
2.2%
invasive melanoma 46
 
2.1%
(Missing) 2088
95.6%

Length

2023-08-15T14:01:31.133200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T14:01:31.225056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
melanoma 95
39.7%
in 49
20.5%
situ 49
20.5%
invasive 46
19.2%

Most occurring characters

ValueCountFrequency (%)
a 236
15.1%
m 190
12.1%
n 190
12.1%
i 190
12.1%
144
9.2%
e 141
9.0%
l 95
6.1%
o 95
6.1%
s 95
6.1%
v 92
 
5.9%
Other values (2) 98
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1422
90.8%
Space Separator 144
 
9.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 236
16.6%
m 190
13.4%
n 190
13.4%
i 190
13.4%
e 141
9.9%
l 95
6.7%
o 95
6.7%
s 95
6.7%
v 92
 
6.5%
t 49
 
3.4%
Space Separator
ValueCountFrequency (%)
144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1422
90.8%
Common 144
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 236
16.6%
m 190
13.4%
n 190
13.4%
i 190
13.4%
e 141
9.9%
l 95
6.7%
o 95
6.7%
s 95
6.7%
v 92
 
6.5%
t 49
 
3.4%
Common
ValueCountFrequency (%)
144
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1566
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 236
15.1%
m 190
12.1%
n 190
12.1%
i 190
12.1%
144
9.2%
e 141
9.0%
l 95
6.1%
o 95
6.1%
s 95
6.1%
v 92
 
5.9%
Other values (2) 98
6.3%

mel_thick_mm
Real number (ℝ)

Distinct11
Distinct (%)23.9%
Missing2137
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean0.5576087
Minimum0.2
Maximum7.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-08-15T14:01:31.293845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.2
Q10.3
median0.4
Q30.5
95-th percentile0.8
Maximum7.3
Range7.1
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation1.0333316
Coefficient of variation (CV)1.8531483
Kurtosis42.822126
Mean0.5576087
Median Absolute Deviation (MAD)0.1
Skewness6.4439818
Sum25.65
Variance1.0677742
MonotonicityNot monotonic
2023-08-15T14:01:31.374673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.4 11
 
0.5%
0.3 10
 
0.5%
0.2 7
 
0.3%
0.5 6
 
0.3%
0.8 4
 
0.2%
0.35 2
 
0.1%
0.25 2
 
0.1%
0.45 1
 
< 0.1%
1 1
 
< 0.1%
7.3 1
 
< 0.1%
(Missing) 2137
97.9%
ValueCountFrequency (%)
0.2 7
0.3%
0.25 2
 
0.1%
0.3 10
0.5%
0.35 2
 
0.1%
0.4 11
0.5%
0.45 1
 
< 0.1%
0.5 6
0.3%
0.7 1
 
< 0.1%
0.8 4
 
0.2%
1 1
 
< 0.1%
ValueCountFrequency (%)
7.3 1
 
< 0.1%
1 1
 
< 0.1%
0.8 4
 
0.2%
0.7 1
 
< 0.1%
0.5 6
0.3%
0.45 1
 
< 0.1%
0.4 11
0.5%
0.35 2
 
0.1%
0.3 10
0.5%
0.25 2
 
0.1%

mel_type
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2183
Missing (%)100.0%
Memory size34.1 KiB

mel_ulcer
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)5.0%
Missing2143
Missing (%)98.2%
Memory size34.1 KiB
False
 
39
True
 
1
(Missing)
2143 
ValueCountFrequency (%)
False 39
 
1.8%
True 1
 
< 0.1%
(Missing) 2143
98.2%
2023-08-15T14:01:31.476913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

melanocytic
Boolean

Distinct2
Distinct (%)0.3%
Missing1582
Missing (%)72.5%
Memory size34.1 KiB
True
509 
False
 
92
(Missing)
1582 
ValueCountFrequency (%)
True 509
 
23.3%
False 92
 
4.2%
(Missing) 1582
72.5%
2023-08-15T14:01:31.563245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

nevus_type
Categorical

Distinct4
Distinct (%)50.0%
Missing2175
Missing (%)99.6%
Memory size34.1 KiB
combined
4 
persistent/recurrent
2 
blue
1 
spitz
1 

Length

Max length20
Median length14
Mean length10.125
Min length4

Characters and Unicode

Total characters81
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)25.0%

Sample

1st rowblue
2nd rowpersistent/recurrent
3rd rowcombined
4th rowcombined
5th rowpersistent/recurrent

Common Values

ValueCountFrequency (%)
combined 4
 
0.2%
persistent/recurrent 2
 
0.1%
blue 1
 
< 0.1%
spitz 1
 
< 0.1%
(Missing) 2175
99.6%

Length

2023-08-15T14:01:31.647840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T14:01:31.763396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
combined 4
50.0%
persistent/recurrent 2
25.0%
blue 1
 
12.5%
spitz 1
 
12.5%

Most occurring characters

ValueCountFrequency (%)
e 13
16.0%
n 8
9.9%
r 8
9.9%
i 7
8.6%
t 7
8.6%
c 6
7.4%
b 5
 
6.2%
s 5
 
6.2%
o 4
 
4.9%
m 4
 
4.9%
Other values (6) 14
17.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79
97.5%
Other Punctuation 2
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13
16.5%
n 8
10.1%
r 8
10.1%
i 7
8.9%
t 7
8.9%
c 6
7.6%
b 5
 
6.3%
s 5
 
6.3%
o 4
 
5.1%
m 4
 
5.1%
Other values (5) 12
15.2%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79
97.5%
Common 2
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13
16.5%
n 8
10.1%
r 8
10.1%
i 7
8.9%
t 7
8.9%
c 6
7.6%
b 5
 
6.3%
s 5
 
6.3%
o 4
 
5.1%
m 4
 
5.1%
Other values (5) 12
15.2%
Common
ValueCountFrequency (%)
/ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13
16.0%
n 8
9.9%
r 8
9.9%
i 7
8.6%
t 7
8.6%
c 6
7.4%
b 5
 
6.2%
s 5
 
6.2%
o 4
 
4.9%
m 4
 
4.9%
Other values (6) 14
17.3%

patient_id
Categorical

Distinct699
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Memory size34.1 KiB
IP_1244021
 
74
IP_3562983
 
71
IP_7035571
 
63
IP_6342052
 
62
IP_8663649
 
61
Other values (694)
1852 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters21830
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique429 ?
Unique (%)19.7%

Sample

1st rowIP_4906546
2nd rowIP_1218261
3rd rowIP_1770335
4th rowIP_1218261
5th rowIP_1218261

Common Values

ValueCountFrequency (%)
IP_1244021 74
 
3.4%
IP_3562983 71
 
3.3%
IP_7035571 63
 
2.9%
IP_6342052 62
 
2.8%
IP_8663649 61
 
2.8%
IP_4879325 58
 
2.7%
IP_5805281 57
 
2.6%
IP_2669703 52
 
2.4%
IP_8173459 45
 
2.1%
IP_0612651 42
 
1.9%
Other values (689) 1598
73.2%

Length

2023-08-15T14:01:31.854928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ip_1244021 74
 
3.4%
ip_3562983 71
 
3.3%
ip_7035571 63
 
2.9%
ip_6342052 62
 
2.8%
ip_8663649 61
 
2.8%
ip_4879325 58
 
2.7%
ip_5805281 57
 
2.6%
ip_2669703 52
 
2.4%
ip_8173459 45
 
2.1%
ip_0612651 42
 
1.9%
Other values (689) 1598
73.2%

Most occurring characters

ValueCountFrequency (%)
I 2183
10.0%
P 2183
10.0%
_ 2183
10.0%
2 1719
7.9%
8 1699
7.8%
3 1660
7.6%
6 1605
 
7.4%
0 1571
 
7.2%
5 1535
 
7.0%
4 1428
 
6.5%
Other values (3) 4064
18.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15281
70.0%
Uppercase Letter 4366
 
20.0%
Connector Punctuation 2183
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1719
11.2%
8 1699
11.1%
3 1660
10.9%
6 1605
10.5%
0 1571
10.3%
5 1535
10.0%
4 1428
9.3%
7 1360
8.9%
1 1354
8.9%
9 1350
8.8%
Uppercase Letter
ValueCountFrequency (%)
I 2183
50.0%
P 2183
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17464
80.0%
Latin 4366
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 2183
12.5%
2 1719
9.8%
8 1699
9.7%
3 1660
9.5%
6 1605
9.2%
0 1571
9.0%
5 1535
8.8%
4 1428
8.2%
7 1360
7.8%
1 1354
7.8%
Latin
ValueCountFrequency (%)
I 2183
50.0%
P 2183
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 2183
10.0%
P 2183
10.0%
_ 2183
10.0%
2 1719
7.9%
8 1699
7.8%
3 1660
7.6%
6 1605
 
7.4%
0 1571
 
7.2%
5 1535
 
7.0%
4 1428
 
6.5%
Other values (3) 4064
18.6%

personal_hx_mm
Boolean

Distinct2
Distinct (%)0.1%
Missing19
Missing (%)0.9%
Memory size34.1 KiB
True
1189 
False
975 
(Missing)
 
19
ValueCountFrequency (%)
True 1189
54.5%
False 975
44.7%
(Missing) 19
 
0.9%
2023-08-15T14:01:31.951106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

pixels_x
Real number (ℝ)

Distinct171
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3791.0962
Minimum85
Maximum7360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-08-15T14:01:32.043962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile640
Q11920
median3264
Q36000
95-th percentile6000
Maximum7360
Range7275
Interquartile range (IQR)4080

Descriptive statistics

Standard deviation1895.6691
Coefficient of variation (CV)0.50003192
Kurtosis-1.3336401
Mean3791.0962
Median Absolute Deviation (MAD)1344
Skewness-0.057573239
Sum8275963
Variance3593561.4
MonotonicityNot monotonic
2023-08-15T14:01:32.326716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000 821
37.6%
3264 605
27.7%
1920 325
 
14.9%
640 112
 
5.1%
3024 94
 
4.3%
4032 42
 
1.9%
768 7
 
0.3%
960 5
 
0.2%
1024 3
 
0.1%
1951 2
 
0.1%
Other values (161) 167
 
7.7%
ValueCountFrequency (%)
85 1
< 0.1%
166 1
< 0.1%
169 1
< 0.1%
170 1
< 0.1%
187 1
< 0.1%
190 1
< 0.1%
229 1
< 0.1%
239 1
< 0.1%
253 1
< 0.1%
257 1
< 0.1%
ValueCountFrequency (%)
7360 1
 
< 0.1%
6000 821
37.6%
4128 1
 
< 0.1%
4032 42
 
1.9%
3333 1
 
< 0.1%
3264 605
27.7%
3252 1
 
< 0.1%
3194 1
 
< 0.1%
3024 94
 
4.3%
3014 1
 
< 0.1%

pixels_y
Real number (ℝ)

Distinct165
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2704.5703
Minimum85
Maximum4912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-08-15T14:01:32.441260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile480
Q11145
median2448
Q34000
95-th percentile4000
Maximum4912
Range4827
Interquartile range (IQR)2855

Descriptive statistics

Standard deviation1272.2997
Coefficient of variation (CV)0.47042582
Kurtosis-1.2801842
Mean2704.5703
Median Absolute Deviation (MAD)1552
Skewness-0.37153401
Sum5904077
Variance1618746.5
MonotonicityNot monotonic
2023-08-15T14:01:32.553514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4000 821
37.6%
2448 605
27.7%
1080 325
 
14.9%
480 113
 
5.2%
4032 94
 
4.3%
3024 41
 
1.9%
1024 7
 
0.3%
1280 6
 
0.3%
838 3
 
0.1%
768 3
 
0.1%
Other values (155) 165
 
7.6%
ValueCountFrequency (%)
85 1
< 0.1%
150 1
< 0.1%
170 1
< 0.1%
180 1
< 0.1%
186 1
< 0.1%
199 1
< 0.1%
203 1
< 0.1%
214 1
< 0.1%
228 1
< 0.1%
232 1
< 0.1%
ValueCountFrequency (%)
4912 1
 
< 0.1%
4032 94
 
4.3%
4000 821
37.6%
3694 1
 
< 0.1%
3598 1
 
< 0.1%
3402 1
 
< 0.1%
3247 1
 
< 0.1%
3215 1
 
< 0.1%
3195 1
 
< 0.1%
3121 1
 
< 0.1%

sex
Categorical

Distinct2
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size34.1 KiB
female
1130 
male
1052 

Length

Max length6
Median length6
Mean length5.035747
Min length4

Characters and Unicode

Total characters10988
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowfemale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
female 1130
51.8%
male 1052
48.2%
(Missing) 1
 
< 0.1%

Length

2023-08-15T14:01:32.659875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T14:01:32.768363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female 1130
51.8%
male 1052
48.2%

Most occurring characters

ValueCountFrequency (%)
e 3312
30.1%
m 2182
19.9%
a 2182
19.9%
l 2182
19.9%
f 1130
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10988
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3312
30.1%
m 2182
19.9%
a 2182
19.9%
l 2182
19.9%
f 1130
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 10988
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3312
30.1%
m 2182
19.9%
a 2182
19.9%
l 2182
19.9%
f 1130
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3312
30.1%
m 2182
19.9%
a 2182
19.9%
l 2182
19.9%
f 1130
 
10.3%

mel_mitotic_index
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2183
Missing (%)100.0%
Memory size34.1 KiB

Interactions

2023-08-15T14:01:26.883981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:23.227044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:23.856943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:24.445495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:25.069804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:25.680485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:26.241991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:26.974470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:23.318007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:23.940095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:24.537336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:25.154675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:25.768129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:26.334956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:27.065903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:23.402857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:24.024160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:24.621361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:25.232707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:25.837549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:26.425889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:27.156854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:23.494840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:24.108234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:24.712322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:25.324260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:25.916727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:26.519906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:27.246317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:23.587213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:24.189391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:24.805230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:25.419253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:26.002534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:26.615759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:27.326730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:23.673871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:24.259378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:24.886536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:25.503770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:26.085130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:26.698944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:27.589794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:23.770057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:24.348800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:24.981094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:25.595992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:26.166410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-15T14:01:26.794647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2023-08-15T14:01:27.743439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-15T14:01:28.066253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-15T14:01:28.287494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0isic_idattributioncopyright_licenseacquisition_dayage_approxanatom_site_generalbenign_malignantclin_size_long_diam_mmdermoscopic_typediagnosisdiagnosis_confirm_typefamily_hx_mmimage_typelesion_idmel_classmel_thick_mmmel_typemel_ulcermelanocyticnevus_typepatient_idpersonal_hx_mmpixels_xpixels_ysexmel_mitotic_index
00ISIC_3079785Hospital Italiano de Buenos AiresCC-BY-NCNaN45lower extremitybenignNaNcontact polarizednevushistopathologyFalsedermoscopicIL_3797557NaNNaNNaNNaNNaNNaNIP_4906546False640480maleNaN
11ISIC_2107859Hospital Italiano de Buenos AiresCC-BY-NCNaN65head/neckmalignantNaNNaNmelanomahistopathologyFalseclinicalIL_3211111NaNNaNNaNNaNNaNNaNIP_1218261True12742620maleNaN
22ISIC_3443621Hospital Italiano de Buenos AiresCC-BY-NCNaN85head/neckbenignNaNcontact polarizedsolar lentigohistopathologyFalsedermoscopicIL_3949403NaNNaNNaNNaNNaNNaNIP_1770335False640480femaleNaN
33ISIC_2368449Hospital Italiano de Buenos AiresCC-BY-NCNaN65head/neckmalignantNaNcontact polarizedmelanomahistopathologyFalsedermoscopicIL_3211111NaNNaNNaNNaNNaNNaNIP_1218261True14883059maleNaN
44ISIC_0094098Hospital Italiano de Buenos AiresCC-BY-NCNaN65posterior torsomalignantNaNNaNmelanomahistopathologyFalseclinicalIL_6961144NaNNaNNaNNaNNaNNaNIP_1218261True855661maleNaN
55ISIC_1452632Hospital Italiano de Buenos AiresCC-BY-NCNaN65posterior torsomalignantNaNcontact polarizedmelanomahistopathologyFalsedermoscopicIL_6961144NaNNaNNaNNaNNaNNaNIP_1218261True40321960maleNaN
66ISIC_9098311Hospital Italiano de Buenos AiresCC-BY-NCNaN65head/neckmalignantNaNNaNmelanomahistopathologyFalseclinicalIL_6841868NaNNaNNaNNaNNaNNaNIP_5804995True23582178maleNaN
77ISIC_8786983Hospital Italiano de Buenos AiresCC-BY-NCNaN65head/neckmalignantNaNcontact polarizedmelanomahistopathologyFalsedermoscopicIL_6841868NaNNaNNaNNaNNaNNaNIP_5804995True40323024maleNaN
88ISIC_8793798Hospital Italiano de Buenos AiresCC-BY-NCNaN60NaNmalignantNaNNaNmelanomahistopathologyFalseclinicalIL_3601366NaNNaNNaNNaNNaNNaNIP_0240453True239199femaleNaN
99ISIC_4326975Hospital Italiano de Buenos AiresCC-BY-NCNaN60NaNmalignantNaNcontact polarizedmelanomahistopathologyFalsedermoscopicIL_3601366NaNNaNNaNNaNNaNNaNIP_0240453True435441femaleNaN
Unnamed: 0isic_idattributioncopyright_licenseacquisition_dayage_approxanatom_site_generalbenign_malignantclin_size_long_diam_mmdermoscopic_typediagnosisdiagnosis_confirm_typefamily_hx_mmimage_typelesion_idmel_classmel_thick_mmmel_typemel_ulcermelanocyticnevus_typepatient_idpersonal_hx_mmpixels_xpixels_ysexmel_mitotic_index
7299891ISIC_0305800The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research CentreCC-BY1.065posterior torsobenignNaNcontact non-polarizedNaNserial imaging showing no changeTruedermoscopicIL_4972043NaNNaNNaNNaNNaNNaNIP_6342052True60004000maleNaN
7300892ISIC_3267312The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research CentreCC-BY1.065posterior torsobenignNaNcontact non-polarizedNaNserial imaging showing no changeTruedermoscopicIL_7204328NaNNaNNaNNaNNaNNaNIP_6342052True60004000maleNaN
7301893ISIC_4653049The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research CentreCC-BY1.065posterior torsobenignNaNcontact non-polarizedNaNserial imaging showing no changeTruedermoscopicIL_7210880NaNNaNNaNNaNNaNNaNIP_6342052True60004000maleNaN
7302894ISIC_6147463The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research CentreCC-BY1.065posterior torsobenignNaNcontact non-polarizedNaNserial imaging showing no changeTruedermoscopicIL_5525688NaNNaNNaNNaNNaNNaNIP_6342052True60004000maleNaN
7303895ISIC_0200204The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research CentreCC-BY1.065posterior torsobenignNaNcontact non-polarizedNaNserial imaging showing no changeTruedermoscopicIL_4061088NaNNaNNaNNaNNaNNaNIP_6342052True60004000maleNaN
7304896ISIC_9118358The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research CentreCC-BY1.065posterior torsobenignNaNcontact non-polarizedNaNserial imaging showing no changeTruedermoscopicIL_6954249NaNNaNNaNNaNNaNNaNIP_6342052True60004000maleNaN
7305897ISIC_5559905The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research CentreCC-BY1.065posterior torsobenignNaNcontact non-polarizedNaNserial imaging showing no changeTruedermoscopicIL_7811610NaNNaNNaNNaNNaNNaNIP_6342052True60004000maleNaN
7306898ISIC_3991186The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research CentreCC-BY1.065posterior torsobenignNaNcontact non-polarizedNaNserial imaging showing no changeTruedermoscopicIL_9331932NaNNaNNaNNaNNaNNaNIP_6342052True60004000maleNaN
7307899ISIC_0140339The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research CentreCC-BY1.065posterior torsobenignNaNcontact non-polarizedNaNserial imaging showing no changeTruedermoscopicIL_9981043NaNNaNNaNNaNNaNNaNIP_6342052True60004000maleNaN
7308900ISIC_7150048The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research CentreCC-BY1.065posterior torsobenignNaNcontact non-polarizedNaNserial imaging showing no changeTruedermoscopicIL_7878829NaNNaNNaNNaNNaNNaNIP_6342052True60004000maleNaN